Abstract

We propose a visualization approach for analyzing
players' action behaviors. The proposed approach
consists of two visualization techniques: classical
multidimensional scaling (CMDS) and KeyGraph.
CMDS is for discovering clusters of players who behave
similarly. KeyGraph is for interpreting action
behaviors of players in a cluster of interest. In order
to reduce the dimension of matrices used in computation
of the CMDS input, we exploit a time-series reduction
technique recently proposed by us. Our visualization
approach is evaluated using log of an online
game where three-player types according to Bartle's
taxonomy are found, that is, achievers, explorers, and socializers.

1. Introduction

The market size of online games continues to
experience surging growth [1]. At the same time, competitions among them are also
becoming very high. The quality of player service plays an important role in
winning such competitions. It is therefore inevitable
to online-game developers and publishers to know
their player behaviors so that they can develop game contents that fulfill
player demands. Visualization techniques have been recently applied to discover
in-game player behaviors.

Most work in the literature focuses on visualization
of player trails or time series of visited locations for examining the distance
over time among the members of a social group [2], discovering playing
strategies in a combat game [3], and analyzing movement patterns [4]. Other work focuses on
extracting pathways [5] and on locating clusters of similar players based on
their movement patterns [6].

This research, however, focuses on visualizing player
behaviors based on their actions. According to Bartle's taxonomy [7], online-game players can be
typically identified based on their action behaviors into achievers, explorers,
killers, and socializers. Player-type information should, therefore, be
exploited to provide game contents that players favor, for example, a wider
variety of collectable items for achievers, longer missions for explorers, more
hunting opportunities for killers, and a higher frequency of social events for
socializers. It has been recently reported in [8] that Bartle's taxonomy is
also applicable to social data in a web-based application.

In this paper, we propose an approach for visualizing
players' action behaviors using classical multidimensional scaling (CMDS)
[9] and KeyGraph
[10], both described
in Section 2. First, CMDS is used for locating clusters of similarly behaving
players. KeyGraph is then used for interpreting playing behaviors of players in
a cluster of interest. The input to CMDS is derived based on time-series
matrices of players' action sequences which are needlessly long due to noise
and redundancy, leading to high computational cost. We, therefore, compute the
CMDS input based on reduced time-series matrices obtained by our recently
proposed time-series reduction technique in [11]. To make this paper self-contained, this technique is
described in Section 3. Evaluation of our visualization approach is given in
Section 4, where achievers, explorers, and socializers are found in play log
from an online-game used in the evaluation.

2. Player Visualization

In this
section, we describe CMDS, KeyGraph, log format, and visualization metrics. As
with most other tools for information visualization [12], subjective interpretation
is required for KeyGraph. The described visualization metrics are used for
facilitating this task in Section 4.2.

2.1. Multidimensional Scaling

CMDS is a
prevailing technique for mapping pair-wise relationships to coordinates and has
been applied to several areas such as statistics, psychology, sociology,
political sciences, and marketing [9]. Recently, this technique has been successfully
applied to clustering of online-game players based on their movement patterns
[6]. CMDS takes as its
input matrix ,
indicating dissimilarities between player pairs, and outputs a coordinate
matrix whose configuration minimizes a loss function in preserving all
interpoint distances. Two time series of interest are considered similar if
they have similar rise and fall patterns, although they might have different
scales on the time axis. A good measurement for deriving the distance or
dissimilarities between such series is the dynamic time warping (DTW) distance
[13].

In our research, the th element in is the DTW distance between the reduced
time-series matrices of action sequences of players and .
In addition, we use the function cmdscale in the Statistical Toolbox of Matlab
for performing CMDS and select only the first two dimensions of the constructed
coordinates for plotting players.

2.1.1. Action Coding

Here, we
describe how action sequences are numerically coded into time-series matrices
for computation of DTW distances. Let denote the set of action symbols of interest
and its cardinality. Action sequence is numerically coded into time-series matrix ,
where is a column vector with the element indexing
the action symbol of being 1 and other elements 0.

Consider, for example, the set of action symbols ,
and thus ,
where symbols A, B,
and C are represented by column vectors , ,
and .
In an action sequence such as ,
it is coded into .

2.1.2. Dynamic Time Warping

The DTW
distance between time-series matrices and , ,
having lengths and ,
is defined as follows:where
and is the Euclidean distance between and .

Consider, for example, the set of symbols and two action sequences and .
The DTW distance between corresponding time-series matrices and (c.f., Figure 1), ,
is 5.6, derived as shown in Figure 2.

Figure 1: Time-series matrices and

Figure 2: Derivation of dynamic time
warping distance between and

2.2. Keygraph

KeyGraph is a
visualization tool for discovery of relations among text-based data. Its
underlying concept is based on a building construction metaphor. As shown in
[10], the
precision-recall curve of KeyGraph is superior to TFIDF and NGRAM [14], well-known techniques for
information retrieval, in extraction of correct keywords from a set of
documents. KeyGraph has been later applied to visualize the relations among Web
pages, among products in markets, among earthquake faults, and so forth
[15]. It has also been
successfully applied to identification of player types in an online-game
simulator [16].

Three major
components of KeyGraph are as follows.

(1)Foundations: subgraphs of highly associated
and frequent terms representing basic concepts in the data.(2)Roofs: terms highly associated with
foundations.(3)Columns: associations between foundations and
roofs used for extracting keywords or main concepts in the data.

Associations between terms are defined as the
co-occurrence among them in same sentences, and keywords are the terms in
either foundations or roofs that are connected to strong columns. Under
KeyGraph representation, solid lines and their touching black nodes depict
foundations, dotted lines depict columns, red nodes depict roofs excluding
those in the foundations, and double circles depict keywords. We use a tool
called Polaris [17],
publicly available, for generating KeyGraphs (http://www. chokkan.org/software/dist/polaris-0.19alpha.zip).

Figure 3 shows an example of KeyGraph when it
is applied to the text data taken from the abstract of this paper, where common
preprocessing for text data such as removing of conjunctions, determiners, and
prepositions is performed. From this figure, it can be seen that there is one
foundation consisting of four terms, that is, “KeyGraph,” “action,” “behaviors,” and “players.” The first three terms are also keywords. Another keyword is “proposed.” Three roof terms are
“consists,” “two,” and “techniques.” These terms well
represent the messages in the abstract.

Figure 3: KeyGraph applied to the abstract of this paper.

2.3. Log Format and Visualization Metrics

Player action
sequences in our work are sequences of action symbols extracted from game log,
of an online game discussed in Section 4, that has the following format:
where an event
consists of an action and its object, if any. This format is based on those
adopted in an MMOG simulator in [18] and 3D virtual worlds in [2]. In commercial online games, this kind of game log is
stored in the monitoring database [19].

According to a recent work in [20], there are three categories
of play motivations in online games as follows.

(i)Achievement consisting of three subcomponents,
that is, advancement, mechanics, and competition.(ii)Social consisting of three subcomponents, that
is, socializing, relationship, and teamwork.(iii)Immersion consisting of four subcomponents,
that is, discovery, role-playing, customization, and escapism.
The achievement, social, and immersion categories correspond to Bartle's achievers,
socializers, and explorers, respectively, although the above ten motivations
overlap among player types.

In our work, we focus in particular on
(i)advancement described in [20] as the desire to gain power
progresses rapidly and
accumulates in-game symbols of wealth or status,(ii)socializing described in [20] as having an interest in
helping and chatting with other players, and(iii)discovery described in [20] as finding and knowing
things that most other players do not know about.
This is because
we anticipate that they should be identifiable using our action sequences and
KeyGraphs. Below, we verify this anticipation with simplified data sets and
their KeyGraphs, which serve as our visualization metrics for facilitating
interpretation of KeyGraph results in Section 4.2.

Let us consider a set of action symbols c, w, m, n, r,
standing for chat, walk, interaction with a mission master, interaction with a
nearby object (item, NPC, or monster), and interaction with a remote object,
respectively. The symbol w is a fundamental action and thus should be a
frequent symbol in all action sequences. It is therefore removed from our
consideration. For achievers motivated by advancement, interactions with
mission masters should be frequently seen in their action sequences, and thus
all possible sets of frequent action symbols for them are c, m, m, n, m, r, c,
m, n, c, m, r, m, n, r, and c, m, n, r. For socializers motivated by
socializing, chats should be commonly seen among them, leading to sets of
frequent action symbols c, m, c, n, c, r, c, m, n, c, m, r, c, n, r, and c, m,
n, r. For explorers motivated by discovery, interactions with remote objects
should be commonly seen among them and thus their sets of frequent action
symbols should be c, r, m, r, n, r, c, m, r, c, n, r, m, n, r, and c, m, n, r.

Figure 4 shows the resulting KeyGraphs for the three
player types, where each KeyGraph was generated from the data sets of the
corresponding player type. Note that m, c, and r are the keyword
nodes in the KeyGraphs of achiever, socializer, and
explorer, respectively, and henceforth these findings are used as visualization
metrics.

Figure 4: (a) KeyGraphs for achievers, (b) socializers,
and (c) explorers generated from the given sample data sets, where m
(interaction to a mission master), c (chat), and r (interaction to a remote
object) are the keywords in (a), (b), and (c),
respectively.

3. Time-Series Reduction

Our technique
for obtaining compact sequences representing major player behaviors is based on
Haar wavelet transform [21]. The Haar wavelet transform technique has a wide
range of time-series applications including classification of DNA sequences [22], which motivated us to
apply the technique to action sequences. Below, we first give an outline of the
Haar wavelet transform and then describe the time-series reduction technique.

In the wavelet transform concept, decomposition
involves obtaining wavelet coefficients from a sequence of interest.
Reconstruction involves recovering the original sequence from obtained
coefficients. Henceforth, it is assumed that the length of a sequence is adjusted so that is a power of 2 and .
The th Haar wavelet coefficient at resolution
order , ,
is derived as
where is the th average at order between two corresponding adjacent values in
order .
With this representation, ,
the original sequence is represented by .
An example of Haar wavelet decomposition of the sequence 6, 8, 2, 7, 6, 5, 4, 3
is shown in Table 1.

Table 1: Example of Haar wavelet transform.

Reconstruction of a given sequence from its Haar
wavelet coefficients and averages is done as follows:

Now, we describe our procedure for reducing the length
of the time-series matrix of an action sequence of interest. For explanation,
we use action sequence as an example, where and thus .

(iii) Reconstruct each row in with selected Haar wavelet coefficients as
follows.

Reconstruction of each row in starts from the coefficient at the lowest
resolution order, that is, ,
to those at the next higher order, and so forth. At a given resolution order,
when the number of remaining coefficients is less
than the number of coefficients in that order, they
are selected based on their total energy value in decreasing order, where total
energy of , ,
is defined aswhere is decomposed at row of .
All other unselected coefficients are then reset to zero. Figure 7 shows
resulting after selection of four coefficients in our
example. Following the recipe in [21], the number of Haar wavelet coefficients used in our
performance evaluation is heuristically set to .

(v) Reduce the size of by sampling down a group of repetitive and
consecutive elements at each reconstructed row to one element. Figure 9 shows
the reduced in our example.

Figure 9: Reduced

Note that the DTW distance between the reduced
time-series matrices of players and is assigned to the th element of the CMDS input matrix discussed in Section 2.1.

4. Evaluation

4.1. Settings

We obtained
player log from the online game The ICE [23], under development at our laboratory. A screen shot
of The ICE and the game map in use are shown in Figures 10 and 11,
respectively. The main game objects were nonplayer characters (NPCs),
statically positioned at different locations, with whom player characters (PCs)
must interact—(chat, help, trade)—to receive and complete missions; the
item-shop, from which PCs bought items and monsters, randomly positioned
throughout the game world for PCs to attack with snowballs. Major missions in
the game are as follows.

Figure 10: A screen shot of The ICE.

Figure 11: Map and the positions of NPCs and monsters.

(i)Item delivery where the PC must deliver an
item from the mission issuing NPC to a specified NPC.(ii)Item trade where the PC must trade with NPCs
to increase the amount of money initially provided by the mission issuing NPC.(iii)Monster extermination where the PC must help
the mission issuing NPC by exterminating monsters.

Actions available in The ICE are summarized in Tables
2 and 3. All NPCs are involved in missions, except NPCs 1, 13, 14, and 16. In
the resulting KeyGraphs given in Section 4.2, the symbols for these four
nonmission NPCs and monsters are preceded by “n” for those residing in
Town 1, that is, nH, and “r” for those in Town 2 or the eastern
border of the map, that is, rT, rU, rW, rA, and rD. This is done in order to
utilize the visualization metrics in Section 2.3.

A group of 20 players, on average 20 years of age,
participated in this evaluation. These players consisted of third-year and
fourth-year computer science undergraduate students who were familiar with
online-games but had no experience in playing The ICE. After a brief
introduction to the game, they were asked to arbitrarily play it, starting from
Town 1. In addition to these 20 players, labeled p1–p20, three game masters, JOJO, Justice, KURO,
also participated in the event. In the rest of our evaluation, the symbol w was
removed from the log because it was frequently
present in all players' action sequences and thus bared no information.

4.2. Results and Discussions

Table 4 shows
the mean and variance of time-series matrices of action sequences before and
after the time-series reduction technique is applied.

Table 4: Mean and variance of the data lengths before and after
applying the time-series reduction technique.

Figure 12 plots all players on two-dimensional space
obtained by CMDS. Most players form a cluster on the right half of the figure.
The rests can be considered as outliers, that is, p1, p5, p8, p9, p17. To
remove the effect of these outliers, we excluded them from the log and obtained
a new result in Figure 13. From Figure 13, most players can be divided into
three clusters: cluster 1 of Justice, JOJO, KURO, p10, p15, p20; cluster 2 of
p2, p3, p4, p6; cluster 3 of p7, p16, p18, p19. Each cluster has different
player behaviors as discussed below through interpretation of KeyGraph
visualization results.

Figure 12: MDS result for all data.

Figure 13: MDS result for data after exclusion of the outliers.

4.2.1. Cluster 1: Explorers

Figure 14 shows
the KeyGraph of cluster 1 from which salient features are summarized in the
following.

Figure 14: KeyGraph of
cluster 1, where rD (attacks the boss monster
residing in the most remote map from the initial map) is one of the
keywords.

(i)They moved away from town 1 and fought
monsters 2 and 3.(ii)They also went to the end of the map and
fought the boss monster.(iii)They were not active in pursuing missions.

The above summary is based on our interpretation of
this KeyGraph as follows. First, it can be seen that the foundation of this
KeyGraph is mainly composed of warp and attack (monsters 2 and 3) nodes. Next,
the symbol rD is a keyword indicating that these players went far away to the
end of the map and fought the boss monster there. In addition, because there is
only one NPC symbol J in the keywords, these players were not active in
receiving missions, from NPCs, and in pursuing them.

Consequently, it can be stated that the players in
cluster 1 like to explore the world map and that these players have no interest
in pursuing missions and only fight monsters when they find them. This type of
players fits Bartle's explorer.

4.2.2. Cluster 2: Achievers

Figure 15 shows
the KeyGraph of cluster 2 from which salient features are summarized in the
following.

Figure 15: KeyGraph of cluster 2, where L and R (talk to mission NPCs) are among the keywords.

(i)They mainly moved within town 1.(ii)They also fought monsters 2 and 3.(iii)They received a lot of missions.

The above summary is based on our interpretation of
this KeyGraph as follows. First, it can be seen that besides nodes related to
fighting (B, C, E, u, p), nodes of NPCs residing in town 1 (I, J, K) are included
in the foundation of this KeyGraph. This indicates that these players were
mainly in town 1. In addition, the keywords include symbols L and R which
denote NPCs who are involved in several missions.

As a result, it can be stated that the players in
cluster 2 are aggressive in pursuing missions, especially those completable
within or not far away from town 1. This type of players fits Bartle's
achiever.

4.2.3. Cluster 3: Socializers

Figure 16 shows
the KeyGraph of cluster 3 from which salient features are summarized in the
following.

Figure 16: KeyGraph of
cluster 3, where c (chat) is one of the keywords on the foundation.

The above summary is based on our interpretation of
this KeyGraph as follows. First, the foundation of this KeyGraph includes the
symbol c, not seen in the foundation or keywords of the previous two clusters.
This indicates that these players chatted a lot among each other. Next, the
symbol rD is a keyword showing that the players also fought the boss monster.
We have confirmed through directly investigating the log that a group of three
players (p5, p7, p8) and another group of four players (p16, p17, p18, p19)
frequently chatted among their group members and that each group went together
to the end of the map to fight the boss monster.

From the above interpretation, it can be stated that
the players in this cluster like to communicate with others via chats. This
type of players fits Bartle's socializers.

4.3. Computational Complexity

We give here
the computational complexity of the techniques used in our approach.

(i)CMDS: for players, the time complexity of the original
CMDS is . To cope with very large ,
a recently proposed approximation [24] taking an time can be used.(ii)DTW: the time complexity of DTW for computing the distance between two time series
of length and is . We coped with this issue with the
time-series reduction technique in Section 3. This technique can also be used
together with an approximation technique in [25] that introduces lower bounding based on warping
constraints.(iii)KeyGraph: the time complexity of KeyGraph
is , where is the number of action symbol types.(iv)Wavelet: for a time-series of length ,
the Haar wavelet transform has an time.

5. Conclusions and Future Work

Understanding
the player behaviors is an important issue in improving the service quality of
online games. We have proposed a visualization approach that first locates
clusters of players who have similar action behaviors using CMDS and then
interprets such behaviors of a cluster of interest using KeyGraph. To increase
the efficiency in computation of the CMDS input, we have described the use of
the time-series reduction technique proposed recently by us in [11]. Evaluation of the proposed approach has been done using log from The ICE, where three clusters
have been found to fit three of the four Bartle’s player types, that is, achievers, explorers, and
socializers.

Our future work is to apply the proposed approach to
log from commercial online games and to examine if Bartle's player types can be
found. It might also be interesting to investigate log formats whose
information can be used for automatically identifying other types of Nick Yee's
play motivations.

Acknowledgments

This work was supported in part by
Grant-in-Aid for Scientific Research (C) no. 20500146 from the Japan Society for Promotion of Science. The authors would like to thank the
anonymous reviewers for their invaluable comments.

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